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Found 3 papers, 2 papers with code
Segmentation Fault: A Cheap Defense Against Adversarial Machine Learning
Recently published attacks against deep neural networks (DNNs) have stressed the importance of methodologies and tools to assess the security risks of using this technology in critical systems.
Hack The Box: Fooling Deep Learning Abstraction-Based Monitors
Deep learning is a type of machine learning that adapts a deep hierarchy of concepts.
Throttling Malware Families in 2D
Furthermore, visual exploration of different sets of features is useful in assessing the quality of these sets to carry a valid abstract representation, which can be later used in classification and clustering algorithms to achieve a high accuracy.
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